Multi i-TED characterization

Fourth: Per pixel threshold

• In the previous notebook it was proposed to apply thresholds per pixel:
  • To get to a lower overall threshold that should equate to better resolution.
  • To decrease the size of files, which increases greatly for lower threshoilds due to noise.
  • To study if it impacts the considerable increase in resolution present in iTED-C-0 that was hypothesised being related to noise starting the integration window.
• The 888 configuration is compared to 887 and 88c which stands for 887 with some pixels changed manually.
• Studies of alpha activity and noise are added.

pkg_ver = lambda pkg: "{:<20}{:}".format(pkg.__name__,pkg.__version__)

# ROOT
import uproot
print(pkg_ver(uproot))
import ROOT

# Machine Learning
import sklearn
print(pkg_ver(sklearn))
import torch
print(pkg_ver(torch))

# Data science
import scipy
print(pkg_ver(scipy))
import numpy
print(pkg_ver(numpy))
import pandas
print(pkg_ver(pandas))

# Visualizations
import matplotlib
print(pkg_ver(matplotlib))
import matplotlib.pyplot as plt

import tqdm
print(pkg_ver(tqdm))

uproot              4.3.5
Welcome to JupyROOT 6.26/10
sklearn             1.2.2
torch               2.0.0rc5
scipy               1.10.1
numpy               1.24.2
pandas              1.5.3
matplotlib          3.6.3
tqdm                4.62.3

%jsroot

class spectrum:
    
    def __init__(self, TH1D_, iTED_, Crystal_, Configuration_, Window_, Calibration_):  

        self.__TH1D = TH1D_
        self.__iTED = iTED_
        self.__Crystal = Crystal_
        self.__Configuration = Configuration_
        self.__Window = Window_
        self.__Calibration = Calibration_
        
    def __call__(self, ch):
        return numpy.polyval(self.__Calibration[::-1],ch)
    
    def __ch__(self, en):
        p = numpy.poly1d(self.__Calibration[::-1])
        temp  = set(i for i in (p - en).roots if i > 0)
        return list(temp).pop()
    
    def TH1D(self):
        return self.__TH1D
    
    def Calibration(self):
        return self.__Calibration
    
    def iTED(self):
        return self.__iTED
    
    def Crystal(self):
        return self.__Crystal
    
    def Configuration(self):
        return self.__Configuration
    
    def Window(self):
        return self.__Window
    
    def Noise(self, Time_):
        return self.__TH1D.Integral(
            0,
            self.__TH1D.FindBin(self.__ch__(250))
        )/Time_
    
    def Alpha(self, Time_):
        return self.__TH1D.Integral(
            self.__TH1D.FindBin(self.__ch__(1600)),
            self.__TH1D.FindBin(self.__ch__(2800))
        )/Time_
    
    def __repr__(self):
        return "iTED: {},Crystal: {},Configuration: {},Window: {}".format(
            self.__iTED,
            self.__Crystal,
            self.__Configuration,
            self.__Window
        )
    
    def __str__(self):
        return "{}.{}.{}.{}".format(
            self.__iTED,
            self.__Crystal,
            self.__Configuration,
            self.__Window
        )

iTEDA_cal = pandas.read_csv(
    "../../data/2023-03-02/Energy_Calibrations_02_03_2023/Energy_Calibrations_iTEDA.dat",
    delim_whitespace=True,
    names=["P0","P1","P2"]
)

iTEDB_cal = pandas.read_csv(
    "../../data/2023-03-02/Energy_Calibrations_02_03_2023/Energy_Calibrations_iTEDB.dat",
    delim_whitespace=True,
    names=["P0","P1","P2"]
)

iTEDC_cal = pandas.read_csv(
    "../../data/2023-03-02/Energy_Calibrations_02_03_2023/Energy_Calibrations_iTEDC.dat",
    delim_whitespace=True,
    names=["P0","P1","P2"]
)

iTEDD_cal = pandas.read_csv(
    "../../data/2023-03-02/Energy_Calibrations_02_03_2023/Energy_Calibrations_iTEDD.dat",
    delim_whitespace=True,
    names=["P0","P1","P2"]
)

iTED_cal = pandas.concat([iTEDA_cal.T, iTEDB_cal.T, iTEDC_cal.T, iTEDD_cal.T], axis=1, keys=['A', 'B', 'C', 'D'])

iTED_cal

	A					B					C					D
	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4
P0	97.91440	138.744000	164.131000	96.575700	184.627000	123.545000	181.753000	164.939000	162.539000	156.664000	115.328000	147.053000	129.204000	144.779000	132.346000	91.325100	138.429000	121.824000	138.067000	122.738000
P1	1.96942	1.813160	2.304510	1.764130	2.327000	2.228220	2.581200	2.116580	2.010630	2.193700	2.179250	2.005310	2.079170	2.152760	2.311890	2.016440	2.065850	2.071090	2.132460	1.964320
P2	0.00042	0.000665	0.001125	0.000595	0.001193	0.000559	0.001048	0.001154	0.000915	0.000946	0.000469	0.000859	0.000717	0.000836	0.000898	0.000479	0.000809	0.000667	0.000949	0.000705

config = ["888", "887", "88c"]
CW = [100,150,200,250]

ited = ["A","B","C","D"]
crystal = [0,1,2,3,4]

midx = pandas.MultiIndex.from_product(
    [config, CW],
    names=['Configuration', 'Window']
)

mcol = pandas.MultiIndex.from_product(
    [ited, crystal],
    names=['iTED', 'Crystal']
) 

spectra = pandas.DataFrame(index = midx, columns = mcol)

# Access using spectra[iTED,Crystal][Configuration,Window]

iTEDA = {
    "888-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_02_23_T.13_40_46_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW100.root","READ"),
    "888-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_02_23_T.13_40_46_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW150.root","READ"),
    "888-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_02_23_T.13_40_46_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW200.root","READ"),
    "888-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_02_23_T.13_40_46_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW250.root","READ"),
    "88c-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDA-A2_D.2023_03_14_T.14_28_07_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "88c-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDA-A2_D.2023_03_14_T.14_28_07_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "88c-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDA-A2_D.2023_03_14_T.14_28_07_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "88c-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDA-A2_D.2023_03_14_T.14_28_07_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW250.root","READ"),
    "887-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_03_21_T.14_50_48_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "887-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_03_21_T.14_50_48_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "887-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_03_21_T.14_50_48_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "887-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDA-A2_D.2023_03_21_T.14_50_48_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW250.root","READ")
}

iTEDB = {
    "888-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137_AfterReconnecting_gum_iTEDB-middle_D.2023_03_02_T.15_22_31_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW100.root","READ"),
    "888-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137_AfterReconnecting_gum_iTEDB-middle_D.2023_03_02_T.15_22_31_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW150.root","READ"),
    "888-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137_AfterReconnecting_gum_iTEDB-middle_D.2023_03_02_T.15_22_31_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW200.root","READ"),
    "888-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137_AfterReconnecting_gum_iTEDB-middle_D.2023_03_02_T.15_22_31_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW250.root","READ"),
    "88c-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDB-A2_D.2023_03_14_T.14_35_17_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "88c-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDB-A2_D.2023_03_14_T.14_35_17_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "88c-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDB-A2_D.2023_03_14_T.14_35_17_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "88c-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDB-A2_D.2023_03_14_T.14_35_17_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW250.root","READ"),
    "887-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDB-A2_D.2023_03_21_T.14_57_39_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "887-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDB-A2_D.2023_03_21_T.14_57_39_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "887-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDB-A2_D.2023_03_21_T.14_57_39_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "887-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDB-A2_D.2023_03_21_T.14_57_39_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW250.root","READ")
}

iTEDC = {
    "888-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_02_23_T.13_26_03_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW100.root","READ"),
    "888-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_02_23_T.13_26_03_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW150.root","READ"),
    "888-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_02_23_T.13_26_03_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW200.root","READ"),
    "888-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_02_23_T.13_26_03_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW250.root","READ"),
    "88c-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDC-A1_D.2023_03_14_T.14_41_53_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "88c-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDC-A1_D.2023_03_14_T.14_41_53_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "88c-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDC-A1_D.2023_03_14_T.14_41_53_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "88c-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDC-A1_D.2023_03_14_T.14_41_53_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW250.root","READ"),
    "887-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_03_21_T.15_05_52_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "887-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_03_21_T.15_05_52_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "887-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_03_21_T.15_05_52_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "887-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDC-A1_D.2023_03_21_T.15_05_52_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW250.root","READ")
}

iTEDD = {
    "888-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_02_23_T.13_03_18_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW100.root","READ"),
    "888-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_02_23_T.13_03_18_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW150.root","READ"),
    "888-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_02_23_T.13_03_18_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW200.root","READ"),
    "888-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_02_23_T.13_03_18_C.itedABCD_lab_2023.02.22_4.0v_888_300s_CW250.root","READ"),
    "88c-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDD-A1_D.2023_03_14_T.14_48_58_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "88c-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDD-A1_D.2023_03_14_T.14_48_58_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "88c-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDD-A1_D.2023_03_14_T.14_48_58_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "88c-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/CS137-iTEDD-A1_D.2023_03_14_T.14_48_58_C.itedABCD_lab_custom_2023.02.22_4.0v_887_300s_CW250.root","READ"),
    "887-100": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_03_21_T.15_12_02_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW100.root","READ"),
    "887-150": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_03_21_T.15_12_02_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW150.root","READ"),
    "887-200": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_03_21_T.15_12_02_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW200.root","READ"),
    "887-250": ROOT.TFile.Open("../../data/Multi_iTED_characterization/Cs137-iTEDD-A1_D.2023_03_21_T.15_12_02_C.itedABCD_lab_2023.02.22_4.0v_887_300s_CW250.root","READ")
}

for iTED in[0,1,2,3]:
    for Crystal in [0,1,2,3,4]:
        for Configuration in ["888", "887", "88c"]:
            for CW in [100,150,200,250]:
            
                spectra[
                    ["A","B","C","D"][iTED], Crystal
                ][
                    Configuration, CW
                ] = spectrum(
                    [iTEDA,iTEDB,iTEDC,iTEDD][iTED][f"{Configuration}-{CW}"].Get(
                        "{}_{}_amplitude_spectra;1".format(
                            "SCATTERER" if Crystal==0 else "ABSORBER",
                            ["A","B","C","D"][iTED] if Crystal==0 else "{}_{}".format(["A","B","C","D"][iTED],Crystal)
                        )
                    ), 
                    ["A","B","C","D"][iTED], 
                    Crystal, 
                    Configuration, 
                    CW, 
                    iTED_cal[:][["A","B","C","D"][iTED],Crystal]
                )

spectra.dropna(inplace=True)
spectra.applymap(str)

	iTED	A					B					C					D
	Crystal	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4
Configuration	Window
888	100	A.0.888.100	A.1.888.100	A.2.888.100	A.3.888.100	A.4.888.100	B.0.888.100	B.1.888.100	B.2.888.100	B.3.888.100	B.4.888.100	C.0.888.100	C.1.888.100	C.2.888.100	C.3.888.100	C.4.888.100	D.0.888.100	D.1.888.100	D.2.888.100	D.3.888.100	D.4.888.100
	150	A.0.888.150	A.1.888.150	A.2.888.150	A.3.888.150	A.4.888.150	B.0.888.150	B.1.888.150	B.2.888.150	B.3.888.150	B.4.888.150	C.0.888.150	C.1.888.150	C.2.888.150	C.3.888.150	C.4.888.150	D.0.888.150	D.1.888.150	D.2.888.150	D.3.888.150	D.4.888.150
	200	A.0.888.200	A.1.888.200	A.2.888.200	A.3.888.200	A.4.888.200	B.0.888.200	B.1.888.200	B.2.888.200	B.3.888.200	B.4.888.200	C.0.888.200	C.1.888.200	C.2.888.200	C.3.888.200	C.4.888.200	D.0.888.200	D.1.888.200	D.2.888.200	D.3.888.200	D.4.888.200
	250	A.0.888.250	A.1.888.250	A.2.888.250	A.3.888.250	A.4.888.250	B.0.888.250	B.1.888.250	B.2.888.250	B.3.888.250	B.4.888.250	C.0.888.250	C.1.888.250	C.2.888.250	C.3.888.250	C.4.888.250	D.0.888.250	D.1.888.250	D.2.888.250	D.3.888.250	D.4.888.250
887	100	A.0.887.100	A.1.887.100	A.2.887.100	A.3.887.100	A.4.887.100	B.0.887.100	B.1.887.100	B.2.887.100	B.3.887.100	B.4.887.100	C.0.887.100	C.1.887.100	C.2.887.100	C.3.887.100	C.4.887.100	D.0.887.100	D.1.887.100	D.2.887.100	D.3.887.100	D.4.887.100
	150	A.0.887.150	A.1.887.150	A.2.887.150	A.3.887.150	A.4.887.150	B.0.887.150	B.1.887.150	B.2.887.150	B.3.887.150	B.4.887.150	C.0.887.150	C.1.887.150	C.2.887.150	C.3.887.150	C.4.887.150	D.0.887.150	D.1.887.150	D.2.887.150	D.3.887.150	D.4.887.150
	200	A.0.887.200	A.1.887.200	A.2.887.200	A.3.887.200	A.4.887.200	B.0.887.200	B.1.887.200	B.2.887.200	B.3.887.200	B.4.887.200	C.0.887.200	C.1.887.200	C.2.887.200	C.3.887.200	C.4.887.200	D.0.887.200	D.1.887.200	D.2.887.200	D.3.887.200	D.4.887.200
	250	A.0.887.250	A.1.887.250	A.2.887.250	A.3.887.250	A.4.887.250	B.0.887.250	B.1.887.250	B.2.887.250	B.3.887.250	B.4.887.250	C.0.887.250	C.1.887.250	C.2.887.250	C.3.887.250	C.4.887.250	D.0.887.250	D.1.887.250	D.2.887.250	D.3.887.250	D.4.887.250
88c	100	A.0.88c.100	A.1.88c.100	A.2.88c.100	A.3.88c.100	A.4.88c.100	B.0.88c.100	B.1.88c.100	B.2.88c.100	B.3.88c.100	B.4.88c.100	C.0.88c.100	C.1.88c.100	C.2.88c.100	C.3.88c.100	C.4.88c.100	D.0.88c.100	D.1.88c.100	D.2.88c.100	D.3.88c.100	D.4.88c.100
	150	A.0.88c.150	A.1.88c.150	A.2.88c.150	A.3.88c.150	A.4.88c.150	B.0.88c.150	B.1.88c.150	B.2.88c.150	B.3.88c.150	B.4.88c.150	C.0.88c.150	C.1.88c.150	C.2.88c.150	C.3.88c.150	C.4.88c.150	D.0.88c.150	D.1.88c.150	D.2.88c.150	D.3.88c.150	D.4.88c.150
	200	A.0.88c.200	A.1.88c.200	A.2.88c.200	A.3.88c.200	A.4.88c.200	B.0.88c.200	B.1.88c.200	B.2.88c.200	B.3.88c.200	B.4.88c.200	C.0.88c.200	C.1.88c.200	C.2.88c.200	C.3.88c.200	C.4.88c.200	D.0.88c.200	D.1.88c.200	D.2.88c.200	D.3.88c.200	D.4.88c.200
	250	A.0.88c.250	A.1.88c.250	A.2.88c.250	A.3.88c.250	A.4.88c.250	B.0.88c.250	B.1.88c.250	B.2.88c.250	B.3.88c.250	B.4.88c.250	C.0.88c.250	C.1.88c.250	C.2.88c.250	C.3.88c.250	C.4.88c.250	D.0.88c.250	D.1.88c.250	D.2.88c.250	D.3.88c.250	D.4.88c.250

def get_resolution(cell):
    
    TH1D = cell.TH1D()
    
    TH1D.GetXaxis().SetRange(TH1D.FindBin(100),TH1D.FindBin(400))
    
    MaxBin   = TH1D.FindBin(TH1D.GetMaximumBin())
    
    ADC_Low  = MaxBin-60
    ADC_High = MaxBin+40

    gaussFit = ROOT.TF1("gaussFit", "pol1(0)+gaus(2)", ADC_Low, ADC_High)
    gaussFit.SetParameters(1000,-1,TH1D.GetMaximum(),MaxBin,10)
    TH1D.Fit(gaussFit,"QR")
    
    sigma = abs(gaussFit.GetParameter(4))
    centroid_ch = gaussFit.GetParameter(3)
    
    x1 = cell(centroid_ch+sigma*numpy.sqrt(2*numpy.log(2)))
    x2 = cell(centroid_ch-sigma*numpy.sqrt(2*numpy.log(2)))
    centroid = cell(centroid_ch)
        
    fwhm = x1-x2
            
    return sigma*numpy.sqrt(2*numpy.log(2))*2/centroid_ch*100, (fwhm/centroid)*100, centroid

def TH1D_draw(cell):
    
    TH1D = cell.TH1D()
    
    canvas = ROOT.TCanvas()
    canvas.cd()
    
    TH1D.SetTitle(repr(cell))
    TH1D.SetStats(False)
    
    latex = ROOT.TLatex()
    latex.SetNDC()
    latex.SetTextSize(0.03)
    
    TH1D.Draw("pe")
    
    l1,l2,l3 = get_resolution(cell)
    
    l4 = uproot.open(f"../../data/nTOF_March2022/888/CW100ns/Resolutions_Cs137_CenterScatter_iTED{cell.iTED()}_8.8.8_100ns.root:grResolEnergy;1").values()[1][cell.Crystal()]
    
    latex.DrawText(0.7, 0.8, "R_ch: {:.2f}%".format(l1))
    
    if l4*0.9 < l2 < l4*1.1:
        latex.DrawText(0.7, 0.75, "R_E: {:.2f}%".format(l2))
    else:
        latex.DrawText(0.7, 0.75, "->R_E: {:.2f}%".format(l2))
        
    if 662*(1-l2/100) < l3 < 662*(1+l2/100):
        latex.DrawText(0.7, 0.7, "E: {:.0f}keV".format(l3))
    else:
        latex.DrawText(0.7, 0.7, "->E: {:.0f}keV".format(l3))
        
    latex.DrawText(0.7, 0.65, "R_E(old): {:.2f}%".format(l4))
    latex.DrawText(0.7, 0.6, "Change: {:.2f}%".format(l2-l4))
            
    return canvas

Analysis

Energy resolution @662keV

Using channel values

spectra.applymap(lambda x: get_resolution(x)[0]).T.describe().drop(['count', 'std'], axis=0).style.background_gradient(cmap ='YlOrRd',axis=None)

Configuration	888				887				88c
Window	100	150	200	250	100	150	200	250	100	150	200	250
mean	8.384339	8.284824	8.179738	8.088119	8.404514	8.260254	8.168116	8.069058	8.205656	8.095049	7.989033	7.893597
min	6.572851	6.511189	6.462154	6.437791	6.389740	6.388251	6.388026	6.387243	6.525593	6.523872	6.524571	6.523416
25%	7.075074	7.053681	7.008073	6.964889	7.078504	6.978950	6.931826	6.926262	7.019665	6.955688	6.916974	6.911327
50%	8.225909	8.070114	7.909846	7.781751	8.163990	7.992299	7.856192	7.710460	7.935067	7.823580	7.733675	7.599282
75%	9.645031	9.582242	9.465428	9.371003	9.864735	9.727948	9.630359	9.407380	9.262555	9.173130	9.039284	8.977927
max	11.434119	11.245839	10.762453	10.524706	11.490752	11.455784	11.441227	11.421454	10.867837	10.729804	10.612429	10.583095

Info in <TCanvas::MakeDefCanvas>:  created default TCanvas with name c1

Using the current calibration (March 2023, 888, 100ns)

resolution = spectra.applymap(lambda x: get_resolution(x)[1])

resolution.T.describe().drop(['count', 'std'], axis=0).style.background_gradient(cmap ='YlOrRd',axis=None)

Configuration	888				887				88c
Window	100	150	200	250	100	150	200	250	100	150	200	250
mean	7.193958	7.111687	7.024524	6.947401	7.098719	6.980382	6.905127	6.822373	7.027980	6.936768	6.848609	6.768283
min	5.690363	5.638254	5.596447	5.575697	5.567239	5.565940	5.565758	5.565070	5.678580	5.677089	5.677704	5.676694
25%	6.242677	6.203176	6.200747	6.188435	6.281600	6.245942	6.150291	6.113693	6.202920	6.187947	6.161628	6.147785
50%	6.982063	6.845986	6.713506	6.655423	6.821029	6.726619	6.615602	6.502844	6.768357	6.593864	6.543667	6.496192
75%	8.067847	7.972242	7.892667	7.716481	7.826232	7.692682	7.529721	7.397640	7.606198	7.504692	7.448275	7.348455
max	9.941246	9.793617	9.386924	9.029415	9.524203	9.044597	9.066551	8.877154	9.467014	9.299971	8.853431	8.514926

resolution.style.background_gradient(cmap ='YlOrRd',axis=None)

	iTED	A					B					C					D
	Crystal	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4
Configuration	Window
888	100	6.993667	5.690363	8.003727	6.264532	8.513630	8.685116	7.545204	7.159496	6.921517	6.970458	9.941246	7.024431	5.801541	8.581080	8.260210	6.604624	6.177112	6.154822	6.158417	6.427960
	150	6.745153	5.638254	7.943185	6.214287	8.491202	8.421423	7.531137	7.111619	6.842958	6.928608	9.793617	6.849014	5.800469	8.484608	8.059413	6.501383	6.169841	6.125247	6.149794	6.432523
	200	6.659179	5.596447	7.917940	6.213641	8.473171	8.180773	7.442451	7.047676	6.765897	6.889133	9.386924	6.661115	5.802972	8.298828	7.884243	6.448186	6.162062	6.119100	6.145520	6.395222
	250	6.591387	5.575697	7.894197	6.200813	8.431370	8.083150	7.397516	6.999044	6.719459	6.856619	9.029415	6.493300	5.801645	8.054665	7.657242	6.372846	6.151302	6.100846	6.131791	6.405715
887	100	6.716906	5.885172	7.846681	6.556772	8.926923	8.910126	7.538841	6.771155	7.097204	6.870904	9.524203	6.960888	5.567239	8.476957	7.819416	6.328728	6.363272	5.773790	6.140214	5.898985
	150	6.535668	5.800937	7.777560	6.451929	8.902208	8.430194	7.412162	6.688939	7.037385	6.849367	9.044597	6.764300	5.565940	8.303461	7.664389	6.292431	6.348265	5.763566	6.106475	5.867876
	200	6.442909	5.771719	7.716941	6.419184	8.891864	8.231809	7.349736	6.622017	6.947128	6.741413	9.066551	6.609188	5.565758	8.082529	7.467314	6.171867	6.333189	5.742967	6.085562	5.842901
	250	6.400100	5.753039	7.708582	6.419219	8.877154	8.104824	7.293993	6.586469	6.829195	6.727379	8.635466	6.402065	5.565070	7.867197	7.206193	6.120226	6.323636	5.721918	6.094094	5.811644
88c	100	6.848400	6.095302	7.536584	6.269364	8.602156	8.331459	7.549031	6.688313	6.852857	6.632140	9.467014	7.046612	5.678580	8.494920	7.777696	6.421585	6.130319	5.779793	6.227103	6.130370
	150	6.623912	6.037643	7.482337	6.213026	8.631772	8.207789	7.438936	6.593293	6.594435	6.587102	9.299971	6.840054	5.677089	8.397128	7.571756	6.326870	6.111083	5.770381	6.218071	6.112710
	200	6.583704	6.009039	7.407379	6.181999	8.537732	7.989468	7.375075	6.536205	6.551128	6.534113	8.853431	6.661599	5.677704	8.144216	7.570963	6.219945	6.100512	5.753311	6.207167	6.077498
	250	6.509696	5.991072	7.388117	6.165802	8.514926	7.841426	7.285276	6.488878	6.432151	6.536032	8.405071	6.503505	5.676694	7.996617	7.335234	6.191500	6.093731	5.737227	6.195646	6.077063

Energy of the Cesium peak using the current calibration (March 2023, 888, 100ns)

fit = spectra.applymap(lambda x: abs(get_resolution(x)[2]-662)/662*100)

fit.T.describe().drop(['count', 'std'], axis=0).style.background_gradient(cmap ='YlOrRd',axis=None)

Configuration	888				887				88c
Window	100	150	200	250	100	150	200	250	100	150	200	250
mean	2.783928	2.920516	3.030763	3.097189	1.955409	1.920312	1.916828	1.933787	2.357105	2.474974	2.551107	2.606813
min	0.261671	0.273354	0.137220	0.089146	0.029099	0.028333	0.134172	0.095745	0.070286	0.053619	0.043825	0.033794
25%	1.449584	1.852350	1.881064	1.917321	0.325375	0.655081	0.656098	0.700591	1.354216	1.346443	1.336762	1.389725
50%	2.920744	3.069998	3.110387	3.165389	1.821452	1.769053	1.586570	1.651227	2.083279	2.147551	2.184258	2.200536
75%	4.003028	4.287113	4.503136	4.566823	2.765623	2.633653	2.546075	2.561870	3.410597	3.651191	3.736920	3.937449
max	5.426153	5.436203	5.455493	5.461596	6.084582	6.018045	5.972706	5.965306	5.574989	5.593240	5.603651	5.625288

Noise using integral <250keV (Calibration: March 2023, 888, 100ns)

noise = spectra.applymap(lambda x: x.Noise(300))

noise.T.describe().drop(['count', 'std'], axis=0).style.background_gradient(cmap ='YlOrRd',axis=None)

Configuration	888				887				88c
Window	100	150	200	250	100	150	200	250	100	150	200	250
mean	147.405333	136.332833	128.844333	124.132667	142.744833	130.678833	122.841833	118.004833	139.740500	128.302000	120.719500	115.863333
min	52.613333	51.750000	51.196667	50.896667	52.013333	51.530000	51.100000	50.686667	52.420000	51.913333	51.426667	51.176667
25%	110.761667	107.768333	105.264167	103.232500	98.026667	91.974167	90.113333	88.209167	97.673333	92.397500	89.105000	87.293333
50%	131.191667	123.935000	117.980000	114.606667	122.870000	118.603333	115.718333	111.946667	118.551667	114.095000	111.803333	107.418333
75%	178.795833	168.554167	154.574167	141.446667	175.365833	151.696667	145.940000	141.066667	170.030833	151.110833	143.170833	138.040000
max	295.216667	259.980000	234.353333	215.840000	305.080000	267.790000	241.153333	223.040000	296.756667	259.183333	233.160000	216.130000

noise.style.background_gradient(cmap ='YlOrRd',axis=None)

	iTED	A					B					C					D
	Crystal	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4
Configuration	Window
888	100	119.183333	130.153333	193.790000	81.096667	52.613333	183.633333	114.953333	150.380000	132.046667	130.336667	252.506667	295.216667	110.406667	208.473333	165.350000	148.220000	177.183333	101.016667	110.880000	90.666667
	150	112.280000	123.680000	185.380000	80.136667	51.750000	166.586667	104.853333	142.573333	122.000000	124.190000	214.490000	259.980000	110.383333	176.540000	134.956667	144.566667	174.456667	100.640000	108.740000	88.473333
	200	106.910000	120.353333	181.266667	79.533333	51.196667	154.656667	98.770000	137.286667	114.793333	119.896667	187.350000	234.353333	110.350000	154.546667	116.063333	141.363333	172.856667	100.326667	107.583333	87.430000
	250	104.893333	118.760000	179.606667	79.036667	50.896667	147.906667	94.893333	133.993333	111.256667	117.956667	169.926667	215.840000	110.343333	138.113333	104.293333	139.293333	172.010000	100.050000	106.750000	86.833333
887	100	131.343333	122.546667	149.586667	85.713333	52.013333	150.950000	89.736667	187.833333	98.993333	107.770000	275.983333	305.080000	113.680000	219.033333	171.210000	123.193333	189.750000	95.126667	99.643333	85.710000
	150	127.100000	116.786667	143.156667	85.006667	51.530000	131.516667	75.943333	177.316667	83.190000	97.453333	233.603333	267.790000	113.690000	186.893333	141.620000	120.420000	185.926667	94.296667	97.183333	83.153333
	200	122.646667	113.746667	139.873333	84.353333	51.100000	119.533333	69.373333	170.060000	74.733333	92.033333	205.216667	241.153333	113.656667	164.140000	122.870000	117.690000	183.463333	93.976667	95.670000	81.546667
	250	120.596667	112.210000	138.350000	84.046667	50.686667	111.683333	64.916667	165.960000	71.266667	89.596667	186.773333	223.040000	113.676667	149.216667	110.460000	116.156667	182.080000	93.543333	95.006667	80.830000
88c	100	124.536667	112.566667	148.650000	80.943333	52.420000	153.496667	97.643333	187.423333	96.133333	102.266667	260.886667	296.756667	108.990000	209.876667	164.233333	125.050000	189.416667	97.683333	98.906667	86.930000
	150	119.236667	108.296667	142.756667	79.963333	51.913333	135.673333	84.203333	176.173333	84.213333	94.896667	220.000000	259.183333	108.953333	177.436667	136.253333	122.663333	185.660000	97.060000	96.603333	84.900000
	200	114.663333	105.933333	138.870000	79.273333	51.426667	122.496667	76.666667	168.580000	77.730000	90.963333	192.916667	233.160000	108.943333	156.073333	117.466667	119.730000	183.883333	96.663333	95.420000	83.530000
	250	111.853333	104.570000	137.356667	78.936667	51.176667	114.453333	72.666667	163.726667	74.293333	88.736667	173.936667	216.130000	108.936667	140.090000	105.900000	118.106667	182.383333	96.403333	94.646667	82.963333

Alpha activity rate using 1600keV < E < 2800keV (Calibration: March 2023, 888, 100ns)

alpha = spectra.applymap(lambda x: x.Alpha(300))

alpha.T.describe().drop(['count', 'std'], axis=0).style.background_gradient(cmap ='YlOrRd',axis=None)

Configuration	888				887				88c
Window	100	150	200	250	100	150	200	250	100	150	200	250
mean	151.097667	150.797667	150.653667	150.514000	154.837500	154.582500	154.394500	154.275000	152.493167	152.222833	151.983333	151.891667
min	23.603333	23.700000	23.723333	23.760000	23.936667	23.973333	24.003333	24.016667	24.230000	24.270000	24.380000	24.436667
25%	95.206667	95.145833	95.174167	95.186667	102.195000	102.238333	102.378333	102.337500	94.688333	94.653333	94.688333	94.721667
50%	176.700000	176.380000	176.303333	175.900000	181.468333	181.301667	181.325000	181.373333	175.650000	175.340000	175.136667	175.093333
75%	216.720833	216.290833	216.127500	216.008333	221.419167	220.749167	220.326667	220.070000	221.795000	221.416667	220.708333	220.585000
max	238.063333	238.066667	238.070000	238.070000	238.666667	238.663333	238.666667	238.666667	240.580000	240.583333	240.583333	240.583333

alpha.style.background_gradient(cmap ='YlOrRd',axis=None)

	iTED	A					B					C					D
	Crystal	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4	0	1	2	3	4
Configuration	Window
888	100	24.860000	148.353333	114.170000	36.073333	31.263333	144.330000	121.743333	209.013333	203.166667	216.080000	150.233333	230.230000	238.063333	218.643333	228.553333	23.603333	222.950000	38.316667	206.363333	215.943333
	150	24.983333	148.320000	114.080000	36.083333	31.283333	143.596667	120.430000	208.673333	202.793333	215.690000	149.966667	229.516667	238.066667	217.843333	227.483333	23.700000	222.893333	38.343333	206.433333	215.773333
	200	25.220000	148.370000	114.116667	36.090000	31.333333	143.350000	119.413333	208.500000	202.830000	215.753333	149.776667	229.026667	238.070000	216.970000	227.050000	23.723333	222.910000	38.346667	206.376667	215.846667
	250	25.290000	148.366667	114.143333	36.110000	31.343333	143.176667	118.800000	208.363333	202.626667	215.776667	149.173333	228.880000	238.070000	216.573333	226.470000	23.760000	222.850000	38.316667	206.370000	215.820000
887	100	24.356667	153.090000	122.986667	36.220000	30.953333	152.093333	134.123333	212.510000	209.846667	222.486667	152.493333	232.793333	238.666667	221.063333	231.833333	23.936667	225.200000	39.820000	213.570000	218.706667
	150	24.453333	153.113333	123.040000	36.230000	31.023333	152.030000	131.930000	212.270000	209.490000	222.096667	152.710000	232.123333	238.663333	220.300000	231.030000	23.973333	225.110000	39.833333	213.546667	218.683333
	200	24.610000	153.103333	123.233333	36.260000	31.046667	151.953333	130.370000	212.096667	209.546667	222.146667	152.266667	231.810000	238.666667	219.720000	230.076667	24.003333	224.970000	39.813333	213.556667	218.640000
	250	24.713333	153.210000	123.183333	36.233333	31.053333	151.973333	129.570000	211.876667	209.536667	222.030000	151.726667	231.156667	238.666667	219.416667	230.063333	24.016667	224.990000	39.800000	213.550000	218.733333
88c	100	24.230000	144.443333	113.343333	34.836667	31.183333	147.626667	123.030000	206.073333	200.206667	218.760000	151.093333	232.263333	240.580000	220.146667	228.853333	24.933333	227.563333	38.723333	215.233333	226.740000
	150	24.270000	144.353333	113.293333	34.786667	31.203333	147.060000	121.280000	205.773333	199.803333	218.590000	150.876667	231.760000	240.583333	219.680000	227.953333	25.010000	227.630000	38.733333	215.190000	226.626667
	200	24.380000	144.273333	113.340000	34.780000	31.230000	146.900000	119.803333	205.500000	199.890000	218.460000	150.383333	231.193333	240.583333	218.746667	227.136667	25.050000	227.530000	38.733333	215.160000	226.593333
	250	24.436667	144.393333	113.380000	34.766667	31.270000	146.443333	118.963333	205.386667	199.816667	218.576667	150.370000	230.890000	240.583333	218.523333	226.910000	25.093333	227.546667	38.746667	215.126667	226.610000

Correlations

df = pandas.concat([resolution,alpha,noise,fit],keys=["Resolution","Alpha","Noise","Fit"]).drop(['888','887'], level=1, axis=0).drop([100,150,200], level=2, axis=0).T

df.corr().style.background_gradient(cmap ='YlOrRd',axis=None)

			Resolution	Alpha	Noise	Fit
		Configuration	88c	88c	88c	88c
		Window	250	250	250	250
	Configuration	Window
Resolution	88c	250	1.000000	-0.103391	0.035082	0.251798
Alpha	88c	250	-0.103391	1.000000	0.361384	-0.096734
Noise	88c	250	0.035082	0.361384	1.000000	0.047501
Fit	88c	250	0.251798	-0.096734	0.047501	1.000000

Draw fits

canvas = spectra.applymap(TH1D_draw)
#canvas.applymap(lambda x: type(x).__name__)

for col in canvas.columns:
    for row in canvas.index:
        canvas.loc[row,col].Draw()

Conclusions:

• File sizes:
  • Decreasing the threshold from 888 to 887 results in a .singles.ldat file with x3 the size.
  • Increasing the threshold manually (88c) using 887 as a base results in a .singles.ldat file with 40% of the size.
  • The sizes of 88c and 888 are comparable.
• Energy resolution:
  • Taking the mean value for the 4 iTEDs with the different integration windows, the energy resolution improves only about .1% when going from 888 to 887.
  • Taking the mean value for the 4 iTEDs with the different integration windows, the energy resolution improves only about .15% when going from 888 to 88c.
  • The energy resolution is above 8% for 3 (iTED-A-4, iTED-B-0 and iTED-C-0) for 887 and 2 (iTED-A-4 and iTED-C-0) for 88c using the 250ns.
• Correlations:
  • There doesn't seem to exist any sort of correlation between energy resolution and noise or alpha rate.